The robotics and vision communities greatly improved video object segmentation over the recent years. The main approaches in video object segmentation could be categorized into semi-supervised and unsupervised approaches. In semi-supervised video object segmentation approaches (e.g., [1]}[2]}[3]}, the method is initialized manually by a segmentation mask in the first few frames, then the segmented object is tracked throughout the video sequence. On the other hand, unsupervised methods [4]}[5]}[6]}[7]} attempt to discover the primary object automatically and segment it through the video sequence. Motion is one of the fundamental cues that can help improve unsupervised video object segmentation. While there has been recent success in deep learning approaches for segmenting motion (e.g., [5]}[6]}[7]}), current approaches depend mainly on prior large-scale training data.
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